Determine Spatiotemporal Causal Interactions In Data

ABSTRACT

Techniques for detecting outliers in data and determining spatiotemporal causal interactions in the data are discussed. A process collects global positioning system (GPS) points in logs and identifies geographical locations to represent the area where the service vehicles travelled with a passenger. The process models traffic patterns by: partitioning the area into regions, segmenting the GPS points from the logs into time bins, and identifying the GPS points associated with transporting the passenger. The process projects the identified GPS points onto the regions to construct links connecting GPS points located in two or more regions. Furthermore, the process builds a three-dimensional unit cube to represent features of each link. The points farthest away from a center of data cluster are detected as outliers, which represent abnormal traffic patterns. The process constructs outlier trees to evaluate relationships of the outliers and determines the spatiotemporal causal interactions in the data.

BACKGROUND

Location-acquisition technologies collect huge volumes of spatiotemporal data in servers, databases, and cloud computing. The location-acquisition technologies use global positioning system (GPS), global system for mobile communications (GSM), Wi-Fi, etc. to enable collecting spatiotemporal data (space and time qualities) of location histories of where people visited and times of visits. The increasing availability of the spatiotemporal data has provided information in multiple ways. For instance, a large number of service vehicles transport passengers to and from various locations. Some service vehicles may be equipped with sensors to record their spatiotemporal data to a centralized server at regular intervals. The sensors may collect the spatiotemporal data in log books, which identify the locations where the service vehicles travelled with the passengers and times of travel.

However, a challenge includes trying to understand unusual spatiotemporal data of the service vehicles. Additional challenges include sparseness of the data for some roads travelled on and distribution skewness of the data for traffic travelled on different roads. Thus, there are opportunities using innovative technologies to analyze the data for valuable information.

SUMMARY

This disclosure describes detecting outliers from spatiotemporal data and evaluating spatiotemporal causal interactions in the outliers being detected. In one aspect, a process collects sequences of global positioning system (GPS) points in logs from service vehicles and identifies geographical locations to represent an area where the service vehicles travelled based on the logs. The process detects the outliers in the GPS points in the geographical locations by: dividing the area into regions based at least in part on major roads, generating links to connect two or more regions based on a number of transitions pertaining to the links for travel between the regions, calculating a score of minimum distort of features for each link in a time frame, and identifying extreme values among the score of minimum distort as temporal outliers.

In another aspect, computer-readable storage media encoded with instructions perform acts to receive sequences of global positioning system (GPS) points from logs of service vehicles and to create a model that simulates a relationship of traffic of the service vehicles travelling through regions in an area. The instructions further include generating a matrix of the regions from the model to: detect the outliers from a graph of the regions, construct outlier trees based on temporal and spatial properties of the detected outliers, and determine spatiotemporal causal relationships from the constructed outlier trees to correspond to abnormal traffic patterns.

In yet another aspect, an outlier application receives user input for an area to detect outliers in spatiotemporal data. The outlier application receives sequences of global positioning system (GPS) points from logs of service vehicles and creates a model of the traffic patterns in an area based on the GPS points. The model partitions regions in the area and constructs transitions of the GPS points from one region to another region. The model also generates links to connect two or more regions based on a number of transitions pertaining to the links for travel and calculates a score of minimum distort of features for each link in a time frame to detect spatiotemporal outliers that correspond to abnormal traffic patterns. Based on this evaluation, recommendations may be provided for diverting traffic to other streets, converting streets to one way streets, adding more subway lines, and the like.

This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The Detailed Description is set forth with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different figures indicates similar or identical items.

FIG. 1 illustrates an architecture to support an example environment to evaluate spatiotemporal causal interactions in data.

FIG. 2 is a flowchart showing example phases to: build a graph of regions, detect outliers from the graph of regions, determine spatiotemporal causal relationships among the outliers, and evaluate how the outliers affect the regions, to be used in the architecture of FIG. 1.

FIG. 3 is a flowchart showing an example process of building the graph of regions based at least in part on trajectory data from the logs of the service vehicles.

FIG. 4 illustrates an example map of a geographical area partitioned into regions based at least on using major roads.

FIG. 5 illustrates an example partitioned map of trajectories shown travelling through the regions.

FIG. 6 illustrates an example graph of regions with links connecting the regions based on transitions being formulated between the regions.

FIG. 7 is a flowchart showing an example process of detecting outliers from the graph of regions.

FIG. 8 illustrates example charts of a number of service vehicles travelling on a link over adjacent days.

FIG. 9 illustrates an example three-dimensional init cube to detect outliers.

FIG. 10 illustrates an example process of building a forest of outlier trees.

FIG. 11 is a block diagram showing an example server usable with the environment of FIG. 1.

DETAILED DESCRIPTION Overview

This disclosure describes a process for detecting outliers by identifying observations that appear to deviate from other points of the spatiotemporal data (i.e., both space and time information) and evaluating causal interactions among the detected spatiotemporal outliers. Next, the process provides recommendations based on an analysis of the outliers detected and the causal interactions among the detected spatiotemporal outliers.

An example of spatiotemporal data includes tracking of moving objects that may occupy a single position at a given time. Here, the process for the tracking may involve recording movements of service vehicles travelling in a geographical area at a given time. For instance, the service vehicles may travel through a specific region in the geographical area during rush hour between 4 p.m. and 6 p.m. Typically, a majority of the service vehicles tend to be equipped with sensors, such as global positioning system (GPS) sensors, which enable recording their movements and their locations to centralized servers at regular intervals.

Outliers may occur by chance in any distribution of data. For example, the process may detect the outliers in a collection of sequences of GPS points collected in logs from the service vehicles. The process builds a graph of regions from the logs to represent the geographical areas travelled by the service vehicles. The process detects unusual traffic patterns, such as the outliers, in the GPS points in the graph of regions based on observations farthest from data cluster in a three-dimensional unit cube. The unusual traffic patterns may reflect abnormal traffic streams on roads in the graph of regions, which may be caused by events such as celebrations in the streets, parades, large-scale business promotions, protests, traffic control, traffic jams, traffic accidents, rush hour congestion, road construction, weather conditions, and the like.

This disclosure also describes evaluating the causal interactions among the detected outliers of the spatiotemporal data. For instance, the process uses a variety of techniques, such as algorithms, to construct outlier causality trees based on temporal and spatial properties of the detected outliers (i.e., unusual traffic patterns). Based on analysis of the spatiotemporal causal relationships from the causality trees, the process provides recommendations to deal with the unusual traffic patterns in the graph of regions.

Initially, the process collects the spatiotemporal data from the service vehicles equipped with the sensors that constantly probe the geographical areas' traffic patterns, such as traffic flows on the roads and city-wide travel patterns of passengers in the service vehicles. The process analyzes trajectories, which are a collection of sequences of time-ordered GPS points moving in geographical locations. The trajectories represent trips with passengers to and from destinations for the service vehicles and imply human knowledge from drivers of the service vehicles. For example, human knowledge may include driving conditions during rush hour, road constructions, congestion of traffic, weather conditions, and the like.

The process creates a model to simulate the traffic patterns and to connect the traffic flows between the regions in the geographical area based on the trajectories from the service vehicles. Then the process detects the outliers from the model to analyze the unusual traffic patterns and possible reasons for the unusual traffic patterns. As mentioned, the process uses a variety of techniques, such as algorithms to build regions, to identify links to connect the regions, and to construct outlier trees. Based on the outliers detected, the process evaluates the causal interactions in the outliers affecting regions in the geographical area. For instance, the process may provide recommendations, such as adding public transportation, constructing additional roads, or converting roads to one way streets.

While aspects of described techniques can be implemented in any number of different computing systems, environments, and/or configurations, implementations are described in the context of the following example computing environment.

Illustrative Environment

FIG. 1 illustrates an example architectural environment 100, in which a process of detecting outliers in the spatiotemporal data may occur. The environment 100 includes the process of collecting logs from service vehicles 102(1)-(N). The service vehicles 102 may include but are not limited to, taxicabs, limousines, and shuttles that transport passengers to and from desired destinations. These types of service vehicles 102 tend to focus on picking up and dropping off passengers in a geographical area. The service vehicles 102 are configured to have sensors to track their movements and geographical locations. For instance, the sensors may include global positioning system (GPS) sensors which record logs of trajectory data 104. The trajectory data 104 includes time-ordered GPS points recorded at regular intervals sent to centralized servers. To better identify effective driving directions in the geographical area, the process parses trajectories from the logs. For instance, a trajectory in the trajectory data 104 may be represented by Tr₁ with GPS points represented by p₁→p₂→p_(n). Drivers of the service vehicles 102 are very familiar with routes and time-variant traffic flows on roads. The drivers know the fastest routes, which are short and quick, but not necessarily the shortest in distance. Thus, the trajectory data 104 represents the routes travelled most often by the drivers in service vehicles 102 in the geographical area.

The trajectory data 104 from the centralized servers may be sent to spatiotemporal server(s) 106(1), 106(2), . . . , 106(S), via a network(s) 108. The spatiotemporal servers 106(1)-(S) may be configured as plural independent servers, or as a collection of servers that are configured to perform larger scale functions accessible by the network(s) 108. The network(s) 108 represents any type of communications network(s), including wire-based networks (e.g., cable), wireless networks (e.g., cellular, satellite), cellular telecommunications network(s), Wi-Fi networks, and IP-based telecommunications network(s).

The spatiotemporal server(s) 106 may be administered or hosted by a network service provider that provides an outlier application 110 to and from the computing device 112. The outlier application 110 processes the trajectory data 104 collected from the logs of the service vehicles 102. Based on the trajectory data 104 being processed, the outlier application 110 identifies geographical locations where the service vehicles 102 have travelled to generate a map of the geographical area. A discussion of building a graph of the regions occurs with reference to FIG. 3.

In the illustrated example, the computing device 112 may include a user interface (UI) 114 that is presented on a display of the computing device 112. The user interface 114 facilitates access to the outlier application 110 that detects outliers in the spatiotemporal data, and identifies spatiotemporal causal interactions in the detected outliers. For instance, the outlier application 110 evaluates the unusual patterns in a geographical area, constructs outlier trees based on the outliers detected in the geographical area, and determines the spatiotemporal causal interactions in the spatiotemporal data based at least in part on the detected outliers, such as unusual patterns in the traffic. A user 116 may employ the UI 114 to submit a request for a specific area from the outlier application 110.

In one implementation, the UI 114 is a browser-based UI that presents a page received from the outlier application 110. The UI 114 shows a representation 118 of outlier data regarding the geographical area and an outlier tree generated from the data.

The trajectory data 104 may be stored in a database, which may be a separate server or may be a representative set of servers 106 that is accessible via the network(s) 108. The database may store information, such as logs for the service vehicle(s) 102, a sequence of global positioning system (GPS) points, trajectory data 104, models, outlier trees, algorithms, other data, spatiotemporal data, and the like.

FIG. 2 is a flowchart of an example process 200 showing high-level functions performed by the outlier application 110. The processes are illustrated as a collection of blocks in logical flowcharts, which represent a sequence of operations that can be implemented in hardware, software, or a combination. For discussion purposes, the processes are described with reference to the computing environment 100 shown in FIG. 1. However, the processes may be performed using different environments and devices. Moreover, the environments and devices described herein may be used to perform different processes.

For ease of understanding, the methods are delineated as separate steps represented as independent blocks in the figures. However, these separately delineated steps should not be construed as necessarily order dependent in their performance. The order in which the process is described is not intended to be construed as a limitation, and any number of the described process blocks may be combined in any order to implement the method, or an alternate method. Moreover, it is also possible for one or more of the provided steps to be omitted.

The outlier application 110 identifies spatiotemporal causal interactions in the data of a geographical area based on detected outliers of unusual traffic patterns from the logs collected from the service vehicles 102. The process 200 may be divided into five phases, an initial phase 202 to build a graph of regions, a second phase 204 to detect outliers from the graph of regions, a third phase 206 to construct outlier trees based on temporal and spatial properties of the detected outliers, a fourth phase 208 to determine spatiotemporal causal relationships among the detected outliers based on the trees, and a fifth phase 210 to provide recommendations based on the spatiotemporal causal relationships. All of the phases may be used in the environment of FIG. 1, may be performed separately or in combination, and without any particular order.

The initial phase 202 is to build a graph of regions. For instance, the outlier application 110 collects the trajectory data 104 from the service vehicles 102. The outlier application 110 then identifies a geographical area travelled by the service vehicles 102 when picking up or dropping off passengers and divides the geographical area into regions based at least in part on major roads. The outlier application 110 builds the graph of regions with a node representing a region and formulating transitions of travelling from a first region to a second region. A link connects the two regions in a transition.

The second phase 204 is to detect outliers from the graph of regions. The outlier application 110 calculates a score of distort for each link in the graph of regions in time frames using an algorithm. A time frame is a set of consecutive time intervals. The score of distort represents non-spatial and non-temporal attributes of each link in each time frame. Meanwhile, the outlier application 110 identifies extreme values among the score of distort of all links in the graph of regions as temporal outliers.

The third phase 206 is to construct outlier trees based on temporal and spatial properties of the detected outliers. The outlier application 110 uses an algorithm to construct a collection of trees (i.e., a forest) and to retrieve possible descendants of a node. The outlier application 110 constructs the outlier trees by using a number of top outliers (i.e., number=3) detected in a same number (i.e., number=3) of consecutive time frames. Thus, the outlier application 110 identifies outlying links as children along with their parents.

The fourth phase 208 is to determine spatiotemporal causal relationships from the outlier trees. The outlier application 110 uses an algorithm to discover frequent subtrees from the constructed outlier trees. The frequent subtrees represent regions with design issues such as abnormal traffic patterns in the graph of regions.

The fifth phase 210 is to provide recommendations based on the spatiotemporal causal relationships. Based on an evaluation, the outlier application 110 may provide recommendations for public transportation systems, divert traffic to less travelled roads, construct additional streets, convert streets to one-way, for the regions. Details are discussed for building the graph of regions with reference to FIGS. 3-6, for detecting outliers from the graph of regions with reference to FIGS. 7-9, for constructing outlier trees from the detected outliers with reference to FIG. 10, and for performing processes discussed with reference to FIG. 11.

Build a Graph of Regions from Trajectory Data of Service Vehicles

FIG. 3 is a flowchart illustrating an example process for phase 202 (discussed at a high level above) of building the graph of regions. The outlier application 110 may use the collected trajectory data 104 from the centralized servers or receive the logs of the trajectory data 104 from the service vehicle companies. Drivers for the service vehicles 102 may be very familiar with the roads and time-variant traffic flows on the roads. Along with their knowledge, the drivers consider other factors, such as traffic flows and signals, accidents, road constructions, direction turns, and the like. By directly following their routes that are well supported by the trajectory data 104, the knowledge of the drivers may be effectively used.

The outlier application 110 identifies the geographical area travelled by the service vehicles 102 having sensors 302. The geographical area from the trajectory data 104 represents roads and streets where the service vehicles 102 travelled transporting passenger(s). For example, GPS sensors record timestamps, coordinates of locations, and status of occupancy of each service vehicle 102 for a GPS point. The GPS point may contain a timestamp of a date with a time in a.m. or p.m. (d), a longitude coordinate (long), a latitude coordinate (lat), and the status of occupancy (o) which may be collected with a low sampling rate every two-five minutes per point at regular intervals, or may be set at shorter or longer intervals. Thus, the GPS point may be represented by p₁=(d, t, long, lat, o). The status of occupancy may be determined by a weight sensor for each of the service vehicles 102 to detect passengers other than the driver, a weight sensor on seats to determine if passengers occupied the seats of the service vehicles 102, an identifier of occupancy associated with fares indicating passengers are present in the service vehicles 102, and the like.

At 304, the outlier application 110 divides the geographical area into regions based on major roads in the geographical area. For instance, the outlier application 110 divides the geographical area into disjointed regions based on the major roads and smaller streets. A map of the geographical area may include but is not limited to, suburban, communities, towns, cities, and the like. An example of a map is shown with reference to FIG. 4.

At 306, the outlier application 110 formulates transitions of the trajectory travelling between the regions. The outlier application 110 associates each trajectory from the trajectory data 104 to a corresponding region in the geographical area. A transition represented by s is generated between two regions if a GPS point represented as p_(i) is a first point in a first region 1 r₁ and a second point represented as p_(j) is in a second region 2 r₂ (i<j). The transition s includes a departure time (p_(i), t_(i)) from the first region, and an arrival time (p_(j), t_(j)) in the second region. The outlier application 110 transfers each trajectory into a sequence of transitions between pairs of regions. For instance, a trajectory may represent travel of the service vehicle 102 through three regions represented as a, b, and c. As a result, two transitions occur which may be represented as a→b and b→c. The transitions are shown with reference to FIG. 5.

At 308, the outlier application 110 generates links to connect the regions based on the transitions formulated. A link includes a pair of regions represented as (Rgn_(o), Rgn_(d)) to indicate a virtual spatial connection between an origin of a region Rgn_(o) and a destination of a region Rgn_(d). The link exists as long as there is at least one service vehicle 102 moving from the origin of the region Rgn_(o) with a departure time to the destination of the region Rgn_(d) with an arrival time. In other words, the outlier application 110 connects two regions with a link when there is a transition generated between two regions.

At 310, the outlier application 110 associates a link to a feature vector of properties. The outlier application 110 may separate the GPS points according to their timestamps. For instance, the outlier application 110 separates the GPS points into two groups, (1) weekdays and (2) weekends and/or holidays. Then the GPS points are further divided according to 30 minute increments into time bins. It is commonly understood that the time of day for travelling on the roads may affect the speed of the service vehicles 102. Traffic patterns are considerably different during rush hour on weekdays as compared to weekends.

The outlier application 110 uses one unit of time bin to represent a 30 minute period (i.e., 48 time bins represent a day). For instance, a time bin j, a link i may be associated with a feature vector represented as {right arrow over (f)}_(i,j) having three properties. The three properties include (a) a total number of objects on this link (i.e., objects moving from the origin of the region to the destination of the region in a time bin), represented as #Obj, (b) a proportion of the objects among all of the objects moving out of the origin region during this time bin represented as Pct_(o), and (c) the proportion of the objects among all of the objects moving into the destination region in this time bin, represented as Pct_(d).

The outlier application 110 builds the graph of regions with a node representing a region and a link representing traffic flow among the regions as shown with reference to FIG. 6.

Example Map of Partitioned Geographical Area into Regions

FIG. 4 illustrates an example map of partitioning the geographical area into regions based at least on using major roads 304. For instance, the outlier application 110 divides the map of the geographical area into disjoint regions, which includes: communities, neighborhoods, subdivisions, roads, streets, and the like. The roads facilitate transportation while the streets facilitate public interaction. The roads include but are not limited to highways and motorways. The streets include but are not limited to pedestrian streets, alleys, city-centre streets, and the like.

In implementations, the outlier application 110 highlights the major roads 400 with a color or a heavy weight line. The major roads 400 may be referred to as a first zone that includes the top communities. Meanwhile, the outlier application 110 highlights the small roads 402 with another color or a medium weight line. The small roads 402 may be referred to a second zone that includes smaller areas, which are at a lower level than the first zone. Also, the outlier application 110 highlights the streets 404 with yet another color or a small weight line. The streets 404 may be referred to as a third zone that includes smallest areas, and at a lower level than the first zone and the second zone.

Formulating Transitions between the Regions

FIG. 5 illustrates an example process of formulating transitions of the trajectory travelling between the regions 306. The figure illustrates a first region represented by alphabet letter a 500, a second region represented by alphabet letter b 502, and a third region represented by alphabet letter c 504. A trajectory Tr₁ 506 traverses from a first node in region a 500 to a second node in region b 502. Another trajectory Tr₁ 508 traverses from the second node in region b 502 to a third node in region c 504.

Turning to the diagram below, the trajectory passing through three regions, region a 500, region b 502, and region c 504 results in two transitions shown as s₁ 506 and s₂ 50. The trajectory 506 traverses from region a 500 to b at 502 to formulate a transition of a→b. The second trajectory 508 traverses from b at 502 to c at 504 to formulate a transition of b→c.

FIG. 6 illustrates the outlier application 110 generating links to connect the regions based on the transitions formulated 308. Each time frame may be comprised of a fixed number of time bins represented by q. For each time bin q, a link may be represented by Lnk_(i)=<Rgn_(o), Rgn_(d)>, which is associated with a feature vector of three properties {right arrow over (f)}_(i,j)=<#Obj, Pct_(o), Pct_(d)>. The first property, #Obj is a total number of objects on the links from objecting moving from the Rgn_(o) to the Rgn_(d). The second property, Pct_(o) represents a proportion of #Obj among all of the objects moving out of Rgn_(o) in this time bin, q. The third property, Pct_(d) represents a proportion of #Obj among all of the objects moving into Rgn_(d) in time bin, q.

A number on each link indicates a number of transitions pertaining to the link. For instance, a property of link a→b may be represented by:

$\langle{{\overset{\rightarrow}{f}}_{i,j} = {\langle{{{\# {Obj}} = 2},{{Pct}_{o} = {\frac{2}{2 + 3} = 0.4}},{{Pct}_{d} = {\frac{2}{2 + 6} = 0.25}}}\rangle}}$

where a number 2 at 600 between region a and region b indicates two transitions pertaining to the link. In yet another example, a number 5 at 602 between region b and region c indicates five transitions pertaining to the link. The links help with the data sparseness problem. Detecting Outliers from the Graph of Regions

FIG. 7 is a flowchart showing phase 204 (discussed at a high level above) of an example process of detecting outliers from the graph of regions. As discussed above, a time frame is a set of consecutive time intervals. Each time frame or time period may be comprised of a fixed number of time bins represented by q. The outlier application 110 denotes a sequence of feature values of a link 700, Lnk_(i) in a time frame of tf_(j), by: F_(i,j)=<{right arrow over (f)}_(i,j−q+1),{right arrow over (f)}_(i,j−q+2), . . . ,{right arrow over (f)}_(i,j)>.

Next, the outlier application 110 calculates a score of distort for each link 702 by first calculating an Euclidean distance of a difference between each feature (i.e., #Obj) of two different time frames pertaining to a same link. This calculation is known as the score of distort and is performed using the graph of regions in the different time frames. The outlier application 110 computes each link against its precedent two time frames and its future two time frames. However, any number of precedent and future time frames may be used in the comparison. The score of distort denoted by minDistort_(ij) is based on the outlier application 110 searching for the minimum difference for a feature between tf_(j) and the same time frames of the same days on consecutive weeks. Thus, minDistort captures special patterns of traffic data that similar behaviors are observed among the same time of different days or the same day of different weeks.

The outlier application 110 uses an algorithm, minDistort, to calculate the score of distort of time sequences. As shown, in line 7 of the minDistort algorithm, the Euclidean distance is computed between two time frames of a link using the equation below:

${{Distance}\left( {{tf}_{j},{tf}_{t},{Link}_{i}} \right)} = \sqrt{\sum\limits_{k = 0}^{q - 1}{{{\overset{\rightarrow}{f}}_{i,{j - k}} - {\overset{\rightarrow}{f}}_{i,{t - k}}}}^{2}}$

The outlier application 110 obtains the score of distort minDistort_(ij), which includes non-spatial and non-temporal attributes of each link in each time frame. The minDistort algorithm for calculating minimum distort of time sequences follows:

Algorithm minDistort: calculating minimum distort of time sequences Input: Link_(i): a link; tf_(j): a time frame; t: number of adjacent weeks to check Output: minDistort_(i,j): the degree of distort for link Link_(i)in time frame tf_(j) 1:  minDist ←+ Infinity: 2:  T ← tf_(j±u weeks,u ε {−t,...,t}) 3:  for All time frames tf_(t)in T do 4:   if tf_(t) overlap with tf_(j) then 5:    Continue; 6:   end if 7:   currentDist ← Distance(tf_(j),tf_(t),Link_(i)); 8:   if currentDist < minDist Then 9:    minDist ← currentDist; 10: end if 11: end for 12: Return minDist;

The outlier application 110 identifies extreme values among minDistort of all links as temporal outliers. The outlier application 110 normalizes (i.e., subtract min value and divide by max value) the features of each links through all of the time bins into the range of [0,1], so any effects of different sizes of a region and different absolute volumes in a region are decoupled. Another advantage of using minDistort is that this prevents examining many repeating patterns where minDistort˜0.

The outlier application 110 creates a three-dimensional unit cube for each time frame 704. The three-dimensional unit cube includes the features of <#Obj, Pct_(o), Pct_(d)>. The three-dimensional unit cube is discussed with reference to FIG. 9.

The outlier application 110 identifies most extreme points as outliers in the three-dimensional cube 706. For instance, the outlier application 110 normalizes the effect of variances among different directions by using Mahalanobis distance to measure the extremeness of data points. Mahalanobis distance finds extreme points of a set of many candidates. The outlier application 110 detects the outliers with links whose features have the largest difference from both their temporal neighbors for using “minDistort” and spatial neighbors for being detected among all links to represent spatiotemporal outliers (STOs). The outlier application 110 identifies the extreme points as outliers based on detecting abnormal links with either too low volumes or too high volumes, since extremeness of points are based on their Mahalanobis distances. Thus, each STO is a spatial link associated with a time frame. Each STO may be represented by its link, Lnk_(i) containing an original region and a destination region and its time frame tf_(j), i.e., STO_(i,j)=<Rgn_(i,o), Rgn_(id), tf_(j)>. The non-spatial and non-temporal attribute values of the STO are very different from values of spatiotemporal neighbors.

FIG. 8 illustrates example charts 800, 802 of a number of service vehicles 102 travelling on a link over adjacent days. The top chart 800 shows #Objects on the y-axis 804, and Timebins on the x-axis 806. One timebin is approximately 30 minutes. A time period of 30 minutes is used but any time increment may be used. The top chart 800 further illustrates time frames 808 which include several timebins.

The outlier application 110 calculates the value of minDistort of the time frame 808 by calculating a smallest difference between the time frame 808 and the times frames at the same time in adjacent days as shown 810, 812.

FIG. 9 illustrates an example three-dimensional cube to detect outliers 704. As previously discussed, the three-dimensional unit cube includes the features of #Obj in x-coordinate 900, Pct_(o) in the y-coordinate 902, and Pct_(d) in the z-coordinate 904 before normalization for computing into the Mahalanobis distance function. The outlier application 110 locates the most “extreme” points among all points as outliers, which are points whose distance are farthest to the center of the data cluster. For instance, points at 906, 908 may be considered as outliers. The points 906, 908 are the minimum distort values of corresponding features.

Constructing Outlier Trees Based on Temporal and Spatial Properties

FIG. 10 illustrates an example process of phase 206 (discussed at a high level above) of building a forest of outlier trees. The outlier application 110 uses an algorithm, referred to as STOTree algorithm, to find outlier dependencies by looking at the relationships for outliers from the earliest time frame through the last time frame. The STOTree algorithm provides insight that an outlier STO₁ is a parent of another outlier STO₂ if (a) STO₁ occurred before STO₂ in time and (b) the outlier STO₁ outliers and the another outlier STO₂ are spatially correlated. The outlier application 110 uses the STOTree algorithm to construct the outlier trees from detected outliers, which results in a collection of trees (i.e., a forest). The STOTree algorithm for constructing outlier trees is shown below:

Algorithm STOTree: constructing all outlier trees Input: STOutlier: a set of spatial-temporal outliers of size t × k where t is the number of time frames, and k is the number of outliers to examine in a time frame. Output: STOTrees: a list of roots of spatial-temporal trees.  1:  STOTrees ← an empty set { };  2:  for Each time frame i(i ε (1,...,t)) do  3:   for Each outlier j(j ε (1,...,k)) in timeframe i do  4:     STORoot_(i,j) ← Find ← FindAllChildren(STOutlier_(i,j),i);  5:     STOTrees ← STOTrees ∪ STORoot_(i,j);  6:   end for  7:  end for  8:  Return STOTrees;   Subroutine: FindAllChildren(STOutlier_(i,j),i)  9:  if Time frame i is the last time frame then 10:   Return STOutlier_(i,j); 11:  end if 12:  STOutlier_(i,j). subnodes ← an empty set { }; 13:  for Each outlier u(u ε (1,...,k)) in time frame i + 1 do 14:   if STOTrees contains STOutlier_(i+1,u) then 15:    continue; 16:   end if 17:   if STOutliers_(i,j),Rgn^(d) == STOutlier_(i+1,u). Rgn^(o) then 18:    STOutlier_(i,j). subnodes ← STOutlier_(i,j). subnodes ∪      FindAllChildren(STOutlier_(i+1,u),i + 1); 19:   end if 20:  end for 21:  Return SToutlier_(i,j);

The STOTree algorithm shows a subroutine at lines 9 to 21 is a recursive function used to retrieve all possible descendants of a node. For each time frame, the recursive function is called on each outlier of a current time frame to compare with each outlier of a next time frame, unless “current” outlier tree already contains outliers of the next time frame shown at lines 14 to 16. The overall time complexity of the outlier tree construction process on each time frame is upper bounded by O(k²) where k represents a number of outliers in a time frame.

The outlier application 110 places no restrictions in the STOTree algorithm for a maximum size of outlier trees, based on assumptions that abnormal events caused by a single accident is not expected to last for a long time and that sizes of outlier trees should not grow infinitely. Typically, a maximum size of outlier trees tends to be small.

The STOTree algorithm executed by a processor, constructs the outlier tree as shown in FIG. 10. At 1000, the STOTree algorithm uses top three outliers in three consecutive time frames, so the input parameters in the STOTree algorithm are k=3 and t=3. The STOTree algorithm starts from time frame 1 shown at line 2 and for each of the top three outlying links shown from lines 3 to 6, A→B, C→D, and E→F 1000. The STOTree algorithm performs searches in time frame 2 from lines 13 to 20 and checks whether there is any following link that can be a child of previous link from lines 17 to 19. The STOTree algorithm finds outlying links B→G and B→E 1002 as children of A→B. The STOTree algorithm further identifies outlying link H→K 1004 in time frame 3 as a child of J→H 1006 in time frame 2.

From the left time frames 1-3, the STOTree algorithm constructs a first outlier tree 1008 and a second outlier tree 1010, which forms a forest. The forest containing all outlier trees may be represented by T.

Determining Spatiotemporal Causal Relationships from the Outlier Trees

The outlier application 110 identifies the most significant and recurring causal relationships corresponding to the most frequent subtrees of T. The outlier application 110 uses an algorithm, Subtree algorithm, to discover the frequent subtrees that occur through node insertion on the trees.

The Subtree algorithm follows:

Algorithm Subtree: discovering frequentsubtree from STOutlier trees Input: STOTrees: a list of roots of spatial-temporal trees; ε: a support threshold for frequent substructure selection. Output: frequentsubtrees: a list of roots of frequent spatial-temporal subtrees.  1:  //Form a list of frequent nodes (i.e. frequent trees of size 1).  2.  numTrees ← number of roots in STOTrees;  3.  frequentNodes ← unique nodes appearing at least numTrees × ε times    in STOTrees.  4:  mergeTarget ← frequentNodes  5:  frequentSubtrees ← an empty set { };  6:  while size(mergeTarge) > 0 do  7:   // Form candidates of frequent subtrees;  8:   subtreeCandidates ← a empty set { };  9:   for each node singleton_(i) in mergeTarge do 10:   for Each root root_(j) in mergeTarget do 11.    if nodeInsertion(root_(j),singleton_(i)) then 12:     subtreeCandidates ← subtreeCandidates ∪ root_(j); 13:    end if 14:   end for 15:  end for 16:  // Filer subtree candidates be threshold of support ε; 17:  Clear mergeTarget; 18:  for Each candidate candidate_(i) in subtreeCandidates do 19:   count ← 0; 20:   for Each root root_(j) in mergeTarget do 21:    if root_(j) contains candidate_(i) then 22:     count ← count + 1; 23:    end if 24:   end for 25:   if count >ε × numTrees then 26:    frequentSubtree ← frequentTrees ∪ candidate_(i); 27:    mergeTarget ← mergeTarget ∪ candidate_(i) 28:   end if 29:  end for 30: end while 31: Return frequentSubtrees;

The Subtree algorithm first finds all single nodes whose support exceeds a threshold ε shown in line 3 to use this set of frequent single nodes to form candidates of frequent subtrees. The “while” iteration from lines 6 to 30 first generates candidates of subtrees from lines 9 to 15, checks the support of each candidate, and then performs filtering from lines 18 to 29 according to the threshold ε.

The outlier application 110 generates subtree candidates by increasing sizes of the subtrees by one by inserting a frequent single node into previous frequent subtrees. This node insertion process may be performed by an algorithm, node insertion algorithm shown below:

Algorithm: node insertion: inserting a node to an outlier tree Input: Root: a root of an outlier tree; Singleton: a node to be inserted. Output: true/false: whether or not the node insertion is successful.  1: if Root. Rgn_(d) equals singleton. Rgn_(o)&& Root.subnodes does not   contain singleton then  2:   Root.subnodes ← Root.subnodes ∪ singleton;  3:   Return true:  4: else  5:  if size(Root.subnodes)==0 then  6:   Return false;  7:  else  8:   for Root of each subnode subRoot in Root.subnodes do  9:    if InsertNode(subRoot, Singleton) then 10:     Return true; 11:    end if 12:   end for 13:  end if 14: end if 15: Return false;

The node insertion algorithm compares a single node with a root of the tree, and inserts the single node as a subnode of the root at lines 1 to 3, if the root can be a parent of the single node and its existing children do not contain the single node. Otherwise, the single node is compared and checked whether it can be inserted into branches below the root (i.e., a recursive process shown in lines 8 to 12). Returning to the subtree algorithm, the frequency of the candidate increases by one if all of the nodes with their immediate subnodes of the candidates have an exact match with a discovered outlier tree from lines 21 to 23.

An outlier causality may be associated with a region origin, a region destination and a time frame caused by a spatiotemporal outlier if the following conditions hold true: the destination of the spatiotemporal outlier is the same as the origin of the outlier causality and the time frames associated with the outlier causality and the spatiotemporal outlier are consecutive to each other and the time frame associated with the spatiotemporal outlier is ahead of the time frame associated with the outlier causality.

The spatiotemporal causal interactions may include abnormal traffic flow due to parades, marches, protests, insufficient number of roads in the regions, insufficient number of lanes on existing roads, insufficient land use, and the like. The outlier application 110 provides recommendations based on the spatiotemporal causal relationships. For example, the outlier application 110 may recommend but is not limited to, diverting traffic to less travelled roads, building additional roads, suggesting a bus route, suggesting a subway line, converting streets to one way streets, adding more lanes to streets, adding another subway line, adding a train stop, and the like.

The techniques described here may be easily adapted to other technologies. For example, the techniques may be used to find outliers in various applications on the Internet, to detect changes in the climate, and to detect medical conditions.

Example Server Implementation

FIG. 11 is a block diagram showing an example server 106 to be used for the outlier application 110. The spatiotemporal server 106 may be configured as any suitable system capable of services, which includes, but is not limited to, implementing the outlier application 110 for detecting outliers and determining spatiotemporal causal interactions in the spatiotemporal data. In one example configuration, the server 106 comprises at least one processor 1100, a memory 1102, and a communication connection(s) 1104. The processor(s) 1100 may be implemented as appropriate in hardware, software, firmware, or combinations thereof. Software or firmware implementations of the processor(s) 1100 may include computer-executable or machine-executable instructions written in any suitable programming language to perform the various functions described.

Similar to that of computing environment 100 of FIG. 1, memory 1102 may store program instructions that are loadable and executable on the processor(s) 1100, as well as data generated during the execution of these programs. Depending on the configuration and type of computing device, memory 1102 may be volatile (such as random access memory (RAM)) and/or non-volatile (such as read-only memory (ROM), flash memory, etc.). Thus, memory 1102 includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data.

The communication connection(s) 1104 may include access to a wide area network (WAN) module, a local area network module (e.g., Wi-Fi), a personal area network module (e.g., Bluetooth), and/or any other suitable communication modules to allow the spatiotemporal server 106 to communicate over the network(s) 108.

Turning to the contents of the memory 1102 in more detail, the memory 1102 may store an operating system 1106, the outlier application module 110, and one or more applications 1108 for implementing all or a part of applications and/or services using the outlier application 110. The one or more other applications 1108 may include an email application, online services, a calendar application, a navigation module, a game, and the like. The memory 1102 in this implementation may also include a traffic patterns module 1110, an algorithms module 1112, and an outlier tree module 1114. The outlier application module 110 may perform the operations described, perform the operations described with reference to the figures or in combination with the traffic patterns module 1110, the algorithms module 1112, and/or the outlier tree module 1114.

The algorithms module 1112 is configured to be executed on the processor 1100 to perform the many functions described above using the different algorithms. For instance, the algorithms module 1112 provides capabilities for the minDistort algorithm, the STOTree algorithm, the Subtree algorithm, and the node insertion algorithm.

The server 106 may also include additional removable storage 1116 and/or non-removable storage 1118 including, but not limited to, magnetic storage, optical disks, and/or tape storage. The disk drives and their associated computer-readable media may provide non-volatile storage of computer readable instructions, data structures, program modules, and other data for the computing devices. In some implementations, the memory 1102 may include multiple different types of memory, such as static random access memory (SRAM), dynamic random access memory (DRAM), or ROM.

Computer-readable media includes, at least, two types of computer-readable media, namely computer storage media and communications media.

Computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information for access by a computing device.

In contrast, communication media may embody computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave, or other transmission mechanism. As defined herein, computer storage media does not include communication media.

The server 106 may include a database 1120 to store the collection of GPS logs, trajectories, graphs, routes, models, maps of areas, outlier trees, and the like. Alternatively, this information may be stored on other databases.

The server 106 as described above may be implemented in various types of systems or networks. For example, the server may be a part of, including but is not limited to, a client-server system, a peer-to-peer computer network, a distributed network, an enterprise architecture, a local area network, a wide area network, a virtual private network, a storage area network, and the like.

Various instructions, methods, techniques, applications, and modules described herein may be implemented as computer-executable instructions that are executable by one or more computers, servers, or computing devices. Generally, program modules include routines, programs, objects, components, data structures, etc. for performing particular tasks or implementing particular abstract data types. These program modules and the like may be executed as native code or may be downloaded and executed, such as in a virtual machine or other just-in-time compilation execution environment. The functionality of the program modules may be combined or distributed as desired in various implementations. An implementation of these modules and techniques may be stored on or transmitted across some form of computer-readable media.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as example forms of implementing the claims. 

What is claimed is:
 1. A method implemented at least partially by a processor, the method comprising: collecting sequences of global positioning system (GPS) points from logs of service vehicles; identifying geographical locations from the GPS points to represent an area where the service vehicles travelled as recorded in the logs; and detecting outliers in the GPS points in the geographical locations by: dividing the area into regions based at least in part on major roads; generating links to connect two or more regions based on a number of transitions pertaining to the links for travel between the regions; calculating a score of minimum distort of features for each link in a time frame; identifying extreme values among the score of minimum distort as temporal outliers.
 2. The method of claim 1, wherein the GPS points are analyzed from similar time spans in the year.
 3. The method of claim 1, wherein the time frame comprises: separating the GPS points into (a) weekdays and (b) weekends and/or holidays of a year; and dividing a time of a day into time bins of thirty minute increments.
 4. The method of claim 1, wherein the number of transitions of the GPS points is associated with a departure time from at least a region of origin and an arrival time in at least a region of destination.
 5. The method of claim 1, wherein the calculating the score of minimum distort includes computing an Euclidean distance to calculate a difference between each feature of two time frames pertaining to a same link.
 6. The method of claim 1, further comprising: creating a three-dimensional cube unit for each time, the three-dimensional cube unit includes a feature vector of (a) a total number of objects on the link, (b) a proportion of the objects among all of the objects moving out of an origin region during a time period, and (c) a proportion of the objects among all of the objects moving into a destination region in the time period, wherein each point represents a link; and identifying extreme points farthest away from a center data cluster as spatiotemporal outliers in the time frame.
 7. The method of claim 1, further comprising: constructing outlier trees based on temporal and spatial properties of spatiotemporal outliers being detected by determining dependencies of the spatiotemporal outliers from an early time frame through a last time frame; determining a first outlier detected is a parent of a second outlier, if the first outlier occurred before the second outlier in time, and the first outlier and the second outlier are spatially correlated; and adding the parent and a child that is dependent on the parent in the outlier trees being constructed.
 8. The method of claim 1, further comprising determining spatiotemporal causal relationships from outlier trees by: constructing outlier trees based on temporal and spatial properties of spatiotemporal outliers being detected by determining dependencies of outliers from an early time frame through a last time frame; and discovering frequent subtrees from the constructed outlier trees that corresponds to a causality and a relationship among the frequent subtrees to represent abnormal traffic patterns in the GPS points.
 9. The method of claim 8, further comprising providing recommendations based on at least on the frequent subtrees being detected of abnormal traffic patterns, the recommendations include diverting traffic to less travelled roads, building additional roads, suggesting a bus route, or suggesting a subway line.
 10. One or more computer-readable storage media encoded with instructions that, when executed by a processor, perform acts comprising: receiving sequences of global positioning system (GPS) points from logs of service vehicles; creating a model that simulates a relationship of traffic of the service vehicles travelling through regions in an area; generating a matrix of the regions from the model to: detect the outliers from a graph of the regions; construct outlier trees based on temporal and spatial properties of the detected outliers; and determine spatiotemporal causal relationships from the constructed outlier trees to correspond to abnormal traffic patterns.
 11. The computer-readable storage media of claim 10, further comprising: formulating a number of transitions of travel between two or more regions; generating links to connect the two or more regions based on the number of transitions; associating a link to a feature vector to create a three dimensional matrix of (a) a total number of objects on the link, (b) a proportion of the objects among all of the objects moving out of an origin region during a time period, and (c) a proportion of the objects among all of the objects moving into a destination region in the time period; and identifying abnormal links as the outliers in the graph of the regions.
 12. The computer-readable storage media of claim 10, further comprising: calculating a score of distort for a link that connects two or more regions by searching for a minimum difference between each feature of a given time period and another time period pertaining to a same link; using an Euclidian distance to calculate the minimum difference between each feature of the given time period and the another time period pertaining to the same link; and computing each link against precedent time periods and future time periods to identify a minimum distort.
 13. The computer-readable storage media of claim 10, further comprising creating the model by: partitioning the area into the regions based at least in part on major roads; segmenting the GPS points from the logs into time bins and identifying the GPS points associated with a passenger in the service vehicles; and projecting the GPS points associated with the passenger onto the regions to create links connecting regions travelled with passenger.
 14. The computer-readable storage media of claim 10, further comprising presenting a user interface to visually provide one or more recommendations for converting streets to one way streets, adding more lanes to streets, adding more roads, adding another subway line, adding a bus stop, or adding a train stop based at least in part on information generated from the spatiotemporal causal relationships.
 15. The computer-readable storage media of claim 10, further comprising: presenting a user interface to receive a user query for an area; searching the model for the area that is represented by a map of the area partitioned by roads and streets; and providing one or more recommendations for adding a subway line, or widening roads.
 16. A system comprising: a memory; a processor coupled to the memory to perform acts comprising: receiving geographical locations from logs of service vehicles, the geographical locations represent an area where the service vehicles travelled with a passenger; accessing a model of traffic patterns in the area by partitioning regions in the area and projecting global positioning system (GPS) points from the logs onto the regions to construct transitions of the GPS points from a first region to a second region; generating links to connect two or more regions based on a number of transitions pertaining to the links for travel with the passenger between the regions; and calculating a score of minimum distort of features for each link in a time frame to detect spatiotemporal outliers.
 17. The system of claim 16, wherein the time frame includes a fixed number of time increments of approximately 30 minutes.
 18. The system of claim 16, wherein a spatiotemporal outlier is a link with non-spatial and non-temporal attribute values that are very different from values of neighbors.
 19. The system of claim 16, further comprising: constructing outlier trees based on temporal and spatial properties of the spatiotemporal outliers being detected by determining dependencies of the spatiotemporal outliers from an early time frame through a last time frame; and determining a first outlier detected is a parent of a second outlier, if the first outlier occurred before the second outlier in time, and the first outlier and the second outlier are spatially correlated.
 20. The system of claim 16, further comprising: constructing outlier trees based on temporal and spatial properties of the spatiotemporal outliers being detected by determining dependencies of outliers from an early time frame through a last time frame; and discovering frequent subtrees from the constructed outlier trees that corresponds to a causality and a relationship among the frequent subtrees to represent abnormal traffic patterns in the GPS points. 